基于支持向量机的特征提取方法研究

基因表达数据的一个重要应用是给组织样本进行分类。在基因表达数据中,基因的数量相对于数据样本的个数通常比较多;也就是说,可以得到变量数（基因数）远远大于样本数的数据矩阵。过高的维数（变量或基因数）将给分类问题带来极大的挑战。本文提出结合一种新的特征提取方法——非相关线性判别式分析方法（ULDA）和支持向量机（SVM）分类算法,对结肠癌组织样本进行分类识别。并同其它方法作了比较研究,结果表明了该方法的可行性和有效性。     

脱水过程中无核白葡萄转录组测序及膜脂降解代谢中关键基因的筛选

该研究以吐鲁番地区无核白葡萄为试验材料,在25 ℃常温和30 ℃热风干燥后,取失水25%、50%时褐变和未褐变的样品。利用转录组测序技术筛选出膜脂降解代谢相关的关键基因,并利用实时荧光定量PCR技术对其进行验证,研究结果显示,转录组测序共获得了11.63亿的clean data,当无核白失水50%时未褐变与褐变的相比,在快速脱水组筛选出718个差异表达基因,慢速脱水组2 259个。将上述基因进行GO功能富集和KEGG富集分析后,筛选出43个膜脂代谢相关的差异基因,归类于5种代谢途径。从已获得的差异基因中最终筛选出乙醛脱氢酶7B4（Aldehyde Dehydrogenase7B4,ALDH7B4）、双半乳糖甘油二酯合成酶1（Digalactose Diglycerol Synthetase1,DGD1）、脂氧合酶（Lipoxygenase,LOX）、磷脂磷酸水解酶2（Lipid Phosphate Phosphatase2,LPP2）、二酰基甘油激酶5（Diacylglycerol Kinase5,DGK5）、非特异性磷脂酶C4（Non-specific Phospholipase C4,NPC4）、磷脂酶Dα1（Phospholipase Dα1,PLDα1）7个膜脂代谢相关的关键基因,经qRT-PCR验证,基因表达趋势与转录组测序结果基本一致。结果表明,膜脂降解代谢相关基因表达量变化对无核白脱水褐变有一定影响。     

多源基因学习的微种群教与学优化及应用

由于微种群教与学优化算法的种群规模较小, 故其种群多样性很难维持. 为提高微种群教与学优化算法的搜索性能, 提出了一种基于多源基因学习的微种群教与学优化算法(micro-population teaching-learning-based optimization based on multi-source gene learning, MTLBO-MGL). 在MTLBO-MGL算法中, 将教阶段和学阶段根据随机选择策略来对个体进行基因水平上的进化操作; 并从基因层面上对种群多样性进行检测和使用稀疏谱聚类方法对种群的每个维度进行聚类. 然后, 根据多样性检测和聚类结果, 选择不同的进化策略来提高所提算法的搜索性能. 在28个测试函数上, 通过将所提算法与其他4种微种群进化算法作对比, 证明了所提算法的整体性能要显著好于所对比的4种算法. 本文还将所提算法应用于无人机三维路径规划问题, 结果表明MTLBO-MGL算法能够在该问题上取得较好结果.     

Preparation,characterization, and properties of disulfide-containing polyethyleneimine grafted carbon nanotubes

Novel functionalized carbon nanotubes (CNTs) were prepared by grafting disulfide-containing polyethyleneimine (SSPEI) to CNTs. The SSPEI were synthesized by Michael addition between cystamine bisacrylamide and low molecular weight branched 1.8 kDa PEI. Three SSPEI grafted carbon nanotubes (CNTs-SSPEI) were successfully prepared through grafting SSPEI to CNTs. The grafted ratios were 32.26%, 43.11%, and 51.50%, respectively. Moreover, the grafted ratio could be tuned by adjusting the CNTs/SSPEI ratio during the process of preparation. The CNTs-SSPEI was characterized using Fourier transform infrared spectroscopy, scanning electron microscopy and thermogravimetric analysis. The CNTs-SSPEI showed better dispersability and stability in water than CNTs. In addition, the SSPEI on the surface of CNTs-SSPEI could be degraded in the presence of dithiothreitol.     

Flexible Cationic Nanoparticles with Photosensitizer Cores for Multifunctional Biomedical Applications

One challenge for multimodal therapy is to develop appropriate multifunctional agents to meet the requirements of potential applications. Photodynamic therapy (PDT) is proven to be an effective way to treat cancers. Diverse polycations, such as ethylenediamine‐functionalized poly(glycidyl methacrylate) (PGED) with plentiful primary amines, secondary amines, and hydroxyl groups, demonstrate good gene transfection performances. Herein, a series of multifunctional cationic nanoparticles (PRP) consisting of photosensitizer cores and PGED shells are readily developed through simple dopamine‐involving processes for versatile bioapplications. A series of experiments demonstrates that PRP nanoparticles are able to effectively mediate gene delivery in different cell lines. PRP nanoparticles are further validated to possess remarkable capability of combined PDT and gene therapy for complementary tumor treatment. In addition, because of their high dispersities in biological matrix, the PRP nanoparticles can also be used for in vitro and in vivo imaging with minimal aggregation‐caused quenching. Therefore, such flexible nanoplatforms with photosensitizer cores and polycationic shells are very promising for multimodal tumor therapy with high efficacy.     

Peptidyl Virus-Like Nanovesicles as Reconfigurable “Trojan Horse” for Targeted siRNA Delivery and Synergistic Inhibition of Cancer Cells

The self-assembly of peptidyl virus-like nanovesicles (pVLNs) composed of highly ordered peptide bilayer membranes that encapsulate the small interfering RNA (siRNA) is reported. The targeting and enzyme-responsive sequences on the bilayer's surface allow the pVLNs to enter cancer cells with high efficiency and control the release of genetic drugs in response to the subcellular environment. By transforming its structure in response to the highly expressed enzyme matrix metalloproteinase 7 (MMP-7) in cancer cells, it helps the siRNA escape from the lysosomes, resulting in a final silencing efficiency of 92%. Moreover, the pVLNs can serve as reconfigurable “Trojan horse” by transforming into membranes triggered by the MMP-7 and disrupting the cytoplasmic structure, thereby achieving synergistic anticancer effects and 96% cancer cell mortality with little damage to normal cells. The pVLNs benefit from their biocompatibility, targeting, and enzyme responsiveness, making them a promising platform for gene therapy and anticancer therapy.     

碳酸双（三氯甲酯）的合成和应用

综述了碳酸双（三氯甲酯）（ＢＴＣ）的制法、性质及其应用。     

Clustering of temporal gene expression data by regularized spline regression and an energy based similarity measure

Clustering analysis of temporal gene expression data is widely used to study dynamic biological systems, such as identifying sets of genes that are regulated by the same mechanism. However, most temporal gene expression data often contain noise, missing data points, and non-uniformly sampled time points, which imposes challenges for traditional clustering methods of extracting meaningful information. In this paper, we introduce an improved clustering approach based on the regularized spline regression and an energy based similarity measure. The proposed approach models each gene expression profile as a B-spline expansion, for which the spline coefficients are estimated by regularized least squares scheme on the observed data. To compensate the inadequate information from noisy and short gene expression data, we use its correlated genes as the test set to choose the optimal number of basis and the regularization parameter. We show that this treatment can help to avoid over-fitting. After fitting the continuous representations of gene expression profiles, we use an energy based similarity measure for clustering. The energy based measure can include the temporal information and relative changes of the time series using the first and second derivatives of the time series. We demonstrate that our method is robust to noise and can produce meaningful clustering results.